Dooyong Cho

Displacement is a key response index for evaluating the serviceability and safety of civil infrastructure. Although vision sensors have emerged as non-contact and cost-effective alternatives to conventional sensing systems, their performance deteriorates under low-illumination and long-distance conditions because reduced spatial resolution and weakened local intensity information make feature extraction and tracking unreliable. To address this limitation, this study investigates the feasibility of integrating a real-world super-resolution (SR) framework into a vision-sensor-based system for dynamic displacement measurement under low-light conditions. A GAN-based RealSR model was employed to restore low-resolution image sequences, and the restored images were processed using a KAZE-based feature tracking approach. The proposed framework was validated through shaking table tests of a three-story frame structure under a low-illumination condition of 120 lux at measurement distances ranging from 10 to 40 m. Dynamic displacement responses induced by a harmonic excitation of 9.0 Hz were estimated and compared with reference measurements obtained from a linear variable differential transformer (LVDT). The results show that the SR-restored images provided more stable feature extraction and more reliable displacement tracking than bicubic upsampling, particularly for natural targets under low-light and long-range conditions. Furthermore, the application of SR improved the measurability of targets at extended distances, demonstrating that real-world SR is an effective preprocessing strategy for robust vision-based displacement measurement under low illumination.

A truck-mounted attenuator (TMA) is a roadside safety device designed to protect road workers and occupants of colliding vehicles. In countries such as those in Europe and the United States, full-scale crash tests of TMAs have been conducted under various conditions, including head-on, offset, and angled impacts. By contrast, only head-on crash tests have been performed in Korea after it introduced TMA test standards in 2014. In this study, occupant risk indices and post-impact vehicle behavior were analyzed under offset and angled impact conditions via numerical simulations and full-scale crash tests. The results indicate that the occupant risk indices decreased under the offset and angled impact conditions, while significant lateral vehicle motion occurred. This behavior may increase the likelihood of secondary collisions with adjacent traffic in real-world crash scenarios. Therefore, offset and angled impacts, which have been considered optional conditions, should be included as mandatory test items in TMA performance evaluations. In addition, further analyses and the establishment of safety measures addressing vehicle lateral behavior are required.

Rear-end collisions involving maintenance vehicles remain a critical source of severe injuries and fatalities in highway work zones. Existing studies on Rear Impact Guards (RIGs) and Truck-Mounted Attenuators (TMAs) have primarily relied on vehicle-based acceleration metrics or low-speed tests, leaving uncertainty regarding their performance under high-energy impact conditions. This study investigates occupant injury risk and vehicle crash behavior through full-scale frontal impact tests conducted at 80 km/h using a 2002 Renault SM520 passenger car against (1) a truck equipped with a RIG and (2) the same truck equipped with a TMA. Hybrid III 50th percentile ATDs, high-speed imaging, and multi-axis accelerometers were employed to measure occupant kinematics and injury responses. Occupant Risk Indices (THIV (Theoretical Head Impact Velocity), ASI (Acceleration Severity Index), PHD (Post-impact Head Deceleration), and ORA (Occupant Ridedown Acceleration)) and the ATD-based HIC36 were evaluated to assess crash severity. The RIG test exhibited severe underride, resulting in an HIC36 value of 1810, far exceeding the FMVSS 208 limit. In contrast, the TMA significantly reduced occupant injury risk, lowering HIC36 by 83.5%, and maintained controlled vehicle deceleration without compartment intrusion. Comparisons between FSM-based indices and ATD-measured injury responses revealed discrepancies in impact timing and occupant motion, highlighting limitations of current evaluation methodologies. The findings demonstrate the necessity of high-speed testing and ATD-based injury assessment for accurately characterizing RIG/TMA performance and provide evidence supporting improvements to roadside safety hardware standards and work-zone protection strategies.

The current civil infrastructure conditions can be assessed through the measurement of displacement using conventional contact-type sensors. To address the disadvantages of traditional sensors, vision-based sensor measurement systems have been derived in numerous studies and proven as an alternative to traditional sensors. Despite the benefits of the vision sensor, it is well known that the accuracy of the vision-based displacement measurement is largely dependent on the camera extrinsic or intrinsic parameters. In this study, the feasibility study of a deep learning-based single image super-resolution (SISR) technique in a vision-based sensor system is conducted to alleviate the low spatial resolution of image frames at long measurement distance ranges. Additionally, its robustness is evaluated using shaking table tests. As a result, it is confirmed that the SISR can reconstruct definite images of natural targets resulting in an extension of the measurement distance range. Additionally, it is determined that the SISR mitigates displacement measurement error in the vision sensor-based measurement system. Based on this fundamental study of SISR in the feature point-based measurement system, further analysis such as modal analysis, damage detection, and so forth should be continued in order to explore the functionality of SR images by applying low-resolution displacement measurement footage.

This study explored the economic influence of introducing fully autonomous trucks (ATs) on the total cost of operation (TCO) from the viewpoint of freight transport operators. We utilized the cost–benefit analysis and scenario planning frameworks using a case study of a middle-mile route between a port and a large logistics distribution center in South Korea, where an autonomous truck pilot project has been running for several years. We employed a data source obtained from Hanjin Transportation Co., LTD., which enables us to reflect the actual expenses of operating freight transportation in the context of the study route. The findings of this study indicate that ATs would attain substantial operation cost savings for freight transport operators across all five scenarios, ranging from the most pessimistic to the most optimistic. Specifically, results of the base scenario reveal that monthly TCO decreases by 56.25, 47.04, 42.97, and 41.73 percent for 1-, 5-, 12-, and 25-ton trucks, respectively. The cost reduction per month was USD 2399 for a 1-ton truck, USD 2891 for a 5-ton truck, and USD 3438 for a 12-ton truck. Even in the most pessimistic scenario, the operation cost savings for operating with ATs in the route are USD 1415, USD 1642, USD 1985, and USD 2222 per month for 1-, 5-, 12-, and 25-ton trucks, respectively. When factoring in changes in cost, the wage reduction for vehicle drivers was substantial (USD 2430, USD 2835, USD 3240, and USD 3645 for 1-, 5-, 12-, and 25-ton trucks, respectively). However, remote operation was a new cost factor that increased TCO, which increased by USD 243,384,357 in the base case. This study’s findings have multiple implications, including (1) informing economic efficiency and productivity of operating autonomous vehicles (AVs), (2) providing insights on indicative cost estimates for AV operations, (3) providing valuable information for making informed investment decisions, and (4) supporting the notion that freight transport is an especially attractive market for AVs.

This study explored how transportation accessibility and traffic volumes for automobiles, buses, and trucks are related. This study employed machine learning techniques, specifically the extreme gradient boosting decision tree model (XGB) and Shapley Values (SHAP), with national data sources in South Korea collected from the Korea Transport Institute, Statistics Korea, and National Spatial Data Infrastructure Portal. Several key findings of feature importance and plots in non-linear relationships are as follows: First, accessibility indicators exhibited around 5 to 10% of feature importance except for Mart (around 50%). Second, better accessibility to public transportation infrastructures, such as bus stops and transit stations, was associated with higher annual average daily traffic (AADT), particularly in metropolitan areas including Seoul and Busan. Third, access to large-scale markets may have unintended effects on traffic volumes for both vehicles and automobiles. Fourth, it was shown that lower rates of AADT were associated with higher accessibility to elementary schools for all three modes of transportation. This study contributes to (1) understanding complex relationships between the variables, (2) emphasizing the role of transportation accessibility in transportation plans and policies, and (3) offering relevant policy implications.

The Sheikh Jaber Al-Ahmad Al-Sabah Causeway consists of an asymmetric cable-stayed bridge (340 m) and a prestressed concrete (PSC) box girder bridge (35.80 km) linking Kuwait City and Northern Kuwait. The full load tests were performed on the PSC box girder bridge and the load scale was set to 14,092 kNm which was 98.47% of the maximum design moment (14,310 kNm). A total of 12 individual 40 tonf dump trucks were exerted on the bridge for the tests. Based on the influence line of the target bridge, displacement sensors, and strain gauges were installed at the points where the maximum bending moment would occur. The collected deflection and strain data were compared with the finite element method analysis to analyze the change in stiffness of the bridge. From the analysis, it was found that higher stiffness behavior was identified compared to the design load.

Some major drawbacks encountered in the synthesis of copper-ceria (Cu-CeO₂)-based Water Gas Shift (WGS) catalyst via the conventional Impregnation (IMP) method are aggregate formation and nanoparticles’ instability. These lead to the poor interaction between Copper and Ceria, thereby impeding the catalytic activity with the inefficient utilization of active sites. To overcome these drawbacks, in this study, we described the synthesis of the Cu-CeO₂ catalyst via the Reverse Microemulsion (RME) method with the help of the organic surfactant. This development of insights and strategies resulted in the preparation of porous particles with uniform size distribution and improved interaction within the composites, which were evident through XRD, XPS, BET Surface area, TPR, TEM and SEM analysis results. Remarkably, the optimum 20% Cu-CeO₂ catalyst prepared by RME method was found to have superior Water Gas Shift (WGS) catalytic activity than the conventionally Impregnated catalyst when their CO conversion efficiencies were tested in WGS reaction at different feed gas compositions with and without CO₂. Moreover, the 20% Cu-CeO₂ sample prepared by RME method exhibited sustained catalytic activity throughout the entire 48 h period without any signs of deactivation. This observation highlights RME method as the potential pathway for developing more effective nanoparticle catalysts for hydrogen production, contributing to the growing demand for clean and sustainable energy sources.

This paper describes a method that precisely estimates the position of images of traffic surveillance camera objects. We suggest a projection method with multiple traffic surveillance cameras through a local coordinate system into a global coordinate system. The transformation of coordinates uses detected objects, parameters of the camera and the geometric information of high- definition (HD) maps. Traffic surveillance cameras that pursue traffic safety and convenience use various sensors to generate traffic information. We suggest a transformation method with images of the camera and HD maps and an evaluation method. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of the camera in collecting video information has become more important due to advances in artificial intelligence (AI) technology. The objects projected from the traffic surveillance camera domain to the HD domain are helpful to identify imperceptible zones, such as blind spots, on roads for autonomous driving assistance. In this study, we proposed to identify and track dynamic objects (vehicles, pedestrian, etc.) with traffic surveillance cameras, and to analyze and provide information about them in various environments. To this end, we conducted the identification of dynamic objects using the Yolov4 and DeepSort algorithms, established real-time multi-user support servers based on Kafka, defined transformation matrices between images and spatial coordinate systems, and implemented map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that multiple traffic surveillance cameras can serve as important sensors to provide relevant information by analyzing road conditions in real-time in terms of road infrastructure beyond a simple monitoring role.