Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget

Emergency Road Network Determination for Seoul Metropolitan Area

Recently, with the increased frequency of disasters, the demand for measures to secure the golden hour after disasters has been increasing. Therefore, it is necessary to plan and select road infrastructures for effective disaster response. The purpose of this study was to determine emergency road networks for rapid rescue, paramedical activity, and resource transfer in the event of an earthquake in Seoul (including nearby areas). Decisions were made to select a suitable emergency road network in Seoul based on the collection and management of earthquake-related data, grid-based quantitative evaluation of factors regarding demands during disasters and provision of response resources, link-based importance evaluation and grouping analysis, and results of grid and link evaluations. Analysis was first conducted on 16 types of disaster demands, including building, facility, demographic, and response resource-provision data. An expert survey was conducted, and each factor was weighted and integrated into the grid structure for grid-based analysis. Roads and bridges that could play critical roles in an earthquake were selected and grouped in the road network for link-based analysis. The final emergency road network was chosen based on the quantitative and qualitative results from the second and third stages

Analysis of Eddy-Current Probe Signals in Steam Generator U-Bend Tubes Using the Finite Element Method

To ensure the integrity and safety of steam generator tubes in nuclear power plants, eddy-current testing is periodically employed. Because steam generators are equipped with thousands of thin-walled tubes, the eddy current is tested using a bobbin probe that can be used at high speed. Steam generator heat pipes in nuclear power plants have different sizes and shapes depending on their row number. In particular, heat pipes in the first row are located inside the steam generator and are of the U-bend type because the radius of the curved pipe is the smallest. A steam generator heat pipe has a thickness of about 1 mm, so a geometrical cross-sectional area change may occur due to residual stress when manufacturing the curved pipe. It is difficult to determine an exact shape because the change in cross-sectional area generated during the manufacturing process varies depending on the position of the pipe and the distortion rate. During eddy-current testing (ECT), to ensure the integrity and safety of the steam generator tubes, the shape change of the bend may cause a noise signal, making it difficult to evaluate defects in the pipe. However, the noise signals generated in this situation are difficult to analyze in a real measurement environment, and difficult to verify by producing a mock-up, which complicates distinguishing a noise signal from a defective signal. To solve this problem, various noise signals were obtained using the electromagnetic analysis method of COMSOL Multiphysics, a commercial program based on numerical analysis of the finite element method, to simulate the environment, including the change in cross-sectional area of the heat pipe. When compared to the actual measurement signal, the accuracy of the simulations improved, and various types of noise signals were detected, which may be helpful for accurate evaluations of defects during actual inspections

Studies on HG Type of Heterodera glycines in Korea

Thirteen soybean cyst nematode (SCN) (Heterodera glycines) populations collected in Korea were examined in their HG type by their reproductivity on 7 Plant Introduction indicators for the identification of HG type. Six HG types were identified, HG type 0, 2, 5, 2.5, 1.2.7, and 2.5.7. HG type 2.5 was the most frequent (4 samples, 30.8%), followed by HG type 2.5.7 (3 samples, 23.0%). About 76.9% of SCN populations were reproduced on PI 88788, followed by PI 209332 (61.5%), PI 548316 (‘Cloud’) (30.8%), and PI 548402 (‘Peking’) (7.7%). No population could reproduce on PI 90763, PI 437654, thus, they could be used for resistant source for developing SCN resistant soybean in Korea

Robust Imitation Learning against Variations in Environment Dynamics

In this paper, we propose a robust imitation learning (IL) framework that improves the robustness of IL when environment dynamics are perturbed. The existing IL framework trained in a single environment can catastrophically fail with perturbations in environment dynamics because it does not capture the situation that underlying environment dynamics can be changed. Our framework effectively deals with environments with varying dynamics by imitating multiple experts in sampled environment dynamics to enhance the robustness in general variations in environment dynamics. In order to robustly imitate the multiple sample experts, we minimize the risk with respect to the Jensen-Shannon divergence between the agent's policy and each of the sample experts. Numerical results show that our algorithm significantly improves robustness against dynamics perturbations compared to conventional IL baselines