In this study, the applications and the limitations of the Ground-penetrating radar (GPR) investigation have been addressed with the main objective of improving the efficient GPR application of subsurface surveys on Korean expressways. The depth of investigation and detection performance of anomalous objects have been studied using two different types of multichannel GPR on the Korean Expressway Corporation’s nondestructive testing testbed for subsurface detection. Based on the field survey, it was found that utilizing the plane view by depth, cross-sectional and longitudinal profile data of the multichannel GPR simultaneously, analysis and evaluation of the GPR signals are more efficient and practical. Although there was a difference in the frequency of use, the precision difference between two GPR is almost similar in the investigation depth and detection performance of the pavement subsurface anomaly. Under an asphalt concrete standard pavement section, the effective depth of cavity detection is 1–1.5 m, while detection under concrete pavement is less than 1.0 m. In addition, there is still a need to calibrate depths using field cores when constructing a 3D underground facility map.

Black ice has recently been identified as a major cause of transportation accidents due to detecting difficulties on the road surface. It is crucial to provide traffic users with black ice warnings beforehand to sustain commuting safety. The identification of black ice, however, is a difficult initiative, since it necessitates the installation of sophisticated monitoring stations and demands frequently manual inspection. In order to build an economical automatic black ice detection technique, the datasets are built upon a variety of weather conditions, including clear, snowy, rainy, and foggy conditions, as well as two distinct forms of pavement: asphalt and concrete pavement. The Mask R-CNN model was performed to construct the black ice detection via image segmentation. The deep learning architecture was constructed based on pre-trained convolutional neural network models (ResNetV2) for black ice detection purposes. Different pretrained models and architecture (Yolov4) were then compared to determine which is superior for image segmentation of black ice. Afterward, through the retrieved bounding box data, the degree of danger area is determined based on the number of segmentation pixels. In general, the training results confirm the feasibility of the black ice detection method via the deep learning technique. Within “Clear” weather conditions, the detecting precision can be achieved up to 92.5%. The results also show that the increase in the number of weather types leads to a noticeable reduction in the training precision. Overall, the proposed image segmentation method is capable of real-time detection and can caution commuters of black ice in advance.

This paper proposes a performance-based mix design (PBMD) framework to support performance-related specifications (PRS) needed to establish relationships between acceptable quality characteristics (AQCs) and predicted performance, as well as to develop fatigue-preferred, rutting-preferred, and performance-balanced mix designs. The framework includes defining performance tests and threshold values, developing asphalt mix designs, identifying available performance levels, conducting sensitivity analysis, establishing the relationships between AQCs and predicted performance, and determining performance targets and AQC values for the three PBMDs using predicted performance criteria. Additionally, the framework recommends selecting the PBMD category for each asphalt layer to minimize pavement distresses. In this study, the proposed PBMD protocol was applied to FHWA accelerated loading facility (ALF) materials using asphalt mixture performance tester (AMPT) equipment coupled with mechanistic models. The study developed nine mix designs with varying design VMAs and air voids using the Bailey method. The cracking and rutting performance of the mix designs were determined by direct tension cyclic (DTC) fatigue testing, triaxial stress sweep (TSS) testing, and viscoelastic continuum damage (S-VECD) and viscoplastic shift models for temperature and stress effects. The study found that adjusting the design VMA was the primary way to achieve required performance targets. For fatigue-preferred mix design, the recommended targets were a cracking area of 0 to 1.9%, a rut depth of 10 mm, and a design VMA of 14.6 to 17.6%. For rutting-preferred mix design, the recommended targets were a cracking area of 18%, a rut depth of 0 to 3.8 mm, and a design VMA of 10.1 to 13.1%. For performance-balanced mix design, the recommended targets were a cracking area of 8.1 to 10.7%, a rut depth of 4.6 to 6.4 mm, and a design VMA of 12.6 to 14.3%. Finally, pavement simulation results verified that the proposed PBMD pavement design with fatigue-preferred mix in the bottom layer, performance-balanced mix in the intermediate layer, and rutting-preferred mix in the surface mix could minimize bottom-up cracking propagation without exceeding the proposed rutting performance criterion for long-life.

Conventional asphalt concrete pavements have deteriorated rapidly due to the current increased traffic and extreme climate impacts. In addition to the upgrading in the construction quality, there is an urgent need to expand the utilization of modified asphalt binders to improve road capacity and traffic safety. The proposed research aims to combine epoxy resin (ER) and crumb rubber powder (CRP) contents into conventional Styrene-butadiene-styrene (SBS)-modified asphalt binder to not only reduce the consumption of normal asphalt binder but also promote the usage of recycled waste material in practice. To cope with this research objective, the ER and CRP were designed at 3% and 5% by weight of asphalt binder, respectively. Various laboratory tests were performed to evaluate the performance of modified mixtures (ERCRP), including the Frequency Sweep Test, Multiple Stressed Creep and Recovery, Dynamic Modulus, Semi-Circular Bending (SCB), and Cantabro Durability Tests. Additionally, an assessment of the modified asphalt concrete pavement via field testbed was conducted through Falling Weight Deflectometer and Ground Penetrating Radar. Overall, by adding the ER and CRP, the strain value of the control reference mix can be reduced up to 31.8% and 28.3% at MSCR 0.1 and 3.1 kPa, respectively. Additionally, the dynamic modulus of the ERCRP-modified samples was approximately 32,267 and 189 MPa, while the value of the reference mixture was 28,730 and 105 MPa at the highest and lowest frequency, respectively, indicating an enhancement under repeated loads. Regarding the SCB test results at 0 °C, the peak stress of the ERCRP-modified mixture was 4.75 MPa, while the value of the reference specimens was only 4.2 MPa, noticing the improved stress-bearing capacity. Based on a full-scale testbed, the FLWD elastic modulus of reinforced pavement shows a novel improvement (6.75%) compared with the control pavement, suggesting a potential application of ERCRP-modified asphalt binder for sustainable development purposes.