This study evaluates adhesion strength under various conditions to ensure adhesion performance during asphalt-pavement maintenance. The adhesion performance of a tack coat varies under various conditions. Therefore, to evaluate its curing behavior, several tests, i.e., evaporation residue rate, tracking, tack-lifter, and shear bond strength tests, were conducted based on the type, amount, and curing time of the tack coat.The result of the evaporation residue rate test shows that, except for the SSC tack coat, RSC-4 and modified tack coats require similar curing times, even though the modified tack coats have a lower moisture content. Additionally, based on the evaporation residue rate, the tracking and track-lifter test results show that approximately 75% curing is required to prevent the loss of the tack coat during asphaltpavement maintenance. After maintenance work is completed, the shear bond strength was measured to evaluate the curing properties of the tack coat. The results show that the amount applied, curing degree, and shear bond strength are proportional, whereas the modified tack coat indicate a significant difference in the strength increase rate depending on the curing degree. Additionally, when dust is attached to the surface of the tack coat, the difference in strength exceeds 20%, depending on the attachment ratio.To achieve the best adhesion performance by the tack coat during maintenance work, the loss of the tack coat should be prevented by implementing the exact curing time determined experimentally, regardless of whether the tack coat is modified, and the surface where the tack coat is applied should be cleaned before application.

This study addresses the critical challenge of enhancing vehicle classification accuracy in traffic surveys by optimizing the conditions for vehicle axle recognition through artificial intelligence. With current governmental traffic surveys facing issues—particularly the misclassification of freight vehicles in systems employing a 12-category vehicle classification—the research proposes an optimal imaging setup to improve axle recognition accuracy. Field data were acquired at busy intersections using specialized equipment, comparing two camera installation heights under fixed conditions. Analysis revealed that a shooting height of 8.5m combined with a 50°angle significantly reduces occlusion and captures comprehensive vehicle features, including the front, side, and upper views, which are essential for reliable deep learning-based classification. The proposed methodology integrates YOLOv8 for vehicle detection and a CNN-based Deep Sort algorithm for tracking, with image extraction occurring every three frames. The axle regions are then segmented and analyzed for inter-axle distances and patterns, enabling classification into 15 categories—including 12 vehicle types and additional classes such as pedestrians, motorcycles, and personal mobility devices. Experimental results, based on a dataset collected at a high-traffic point in Gwangju, South Korea, demonstrate that the optimized conditions yield an overall accuracy of 97.22% and a PR-Curve AUC of 0.88. Notably, the enhanced setup significantly improved the classification performance for complex vehicle types, such as 6-axle dump trucks and semi-trailers, which are prone to misclassification under lower installation heights. The study concludes that optimized imaging conditions combined with advanced deep learning algorithms for axle recognition can substantially improve vehicle classification accuracy. These findings have important implications for traffic management, infrastructure planning, road maintenance, and policy-making by providing a more reliable and precise basis for traffic data analysis.

There are now many seismic observatory stations, excluding the acceleration monitoring network for infrastructures, of more than 300 operated by several public and governmental organizations across South Korea. The features of the site and properties of the stations were not investigated, and they have been assumed or guessed to estimate the site-specific seismic responses during the 2016 Gyeongju and 2017 Pohang earthquake events. For these reasons, various and intensive geotechnical and geophysical investigations have been conducted to quantify the site characteristics at 15 seismic stations selected in southeastern Korea. The VS profiles were, at first, obtained by performing only a downhole seismic test (DHT) at 7 stations, and were compared with those from a surface wave method. Then, the shear wave velocity (VS) profiles were deduced by combining three types of in situ seismic methods composed of a cross-hole seismic test, DHTs, and full-waveform sonic loggings at the 8 other stations, especially to complement the application limits of DHT and reduce the depth-dependent uncertainty in VS profile. The representative site characteristic profiles for each station regarding VS and VP with borehole stratigraphy and density were determined based on robust investigations. Various site parameters related to seismic responses at the seismic stations of interest were obtained for the site-specific geotechnical information, which would be useful to earthquake engineering practices.