최근 국내 겨울철 블랙아이스(Black Ice)로 인해 발생하는 교통사고가 증가하는 추세이며, 한국 도로교통공단 조사 결 과 2016～2020년 겨울철까지 블랙아이스로 인한 사고는 총 4,868건이며, 사상자는 8,938명인 것으로 조사 되었다. 도로상 태에 따라 건조대비 동결상태에서 교통사고 발생시 치사율이 43%로 높게 나타났다. 이러한 사고는 기온이 떨어지는 12 월부터 급증하여, 최저기온이 가장낮은 1월까지 증가한다. 블랙아이스는 도로에 쌓인 눈이 융해(해설)과 동시에 도로 위 각종 이물질과 결합 후 재동결하여 흑색 동결막을 형성하는 것을 말한다. 그 특성상 운전자가 차량내부에서 도로의 상태 를 쉽게 파악할 수 없으며 대부분의 운전자가 차량이 미끄러지기 시작함과 동시에 인지하여 사고가 발생하게 된다. 이에 본 연구에서는 기존 포장체의 미끄럼 저항도를 상태별로 비교 분석하였다. 포장체의 미끄럼 저항성 정도를 파악하기 위 해 영국식 미끄럼저항 시험기 (British Pendulum Tester ; BPT)를 사용하였으며, 포장체의 종류로는 일반적인 밀입도 아스팔트 포장, 배수성 아스팔트 포장, 그루빙(포장 표면에 일정한 규격의 홈을 형성)을 적용한 콘크리트 포장, 그루빙이 없는 콘크리트 포장을 적용하였다. 미끄럼저항 실험은 관련 KS규격 및 ASTM규격에 준하여 실시하되 블랙아이스를 모 사하기위하여 표면온도 영하 2～3℃ 샘플에 강우를 모사한 물을 분사하며 영하 9℃로 10분 동결 후 2mm강수량을 모사 한 수분을 재 분사한 후 시험을 실시하였다.

PURPOSES : The purpose of this study was to develop techniques for forecasting black ice using historical pavement temperature data collected by patrol cars and concurrent atmospheric data provided by the Korea Meteorological Administration. METHODS : To generate baseline data, the physical principle that ice forms when the pavement temperature is negative and lower than the dew-point temperature was exploited. To forecast frost-induced black ice, deep-learning algorithms were created using air, pavement, and dew point temperatures, as well as humidity, wind speed, and the z-value of the historical pavement temperature of the target segment. RESULTS : The suggested forecasting models were evaluated against baseline data generated by the above-mentioned physical principle using pavement temperature and atmospheric data gathered on a national highway in the vicinity of Young-dong in the Chungcheongbukdo province. The accuracies of the forecasting models for the bridge and roadway segments were 94% and 90%, respectively, indicating satisfactory results. CONCLUSIONS : Preventive anti-icing maintenance activities, such as applying anti-icing chemicals or activating road heating systems before roadways are covered with ice (frost), could be possible with the suggested methodologies. As a result, traffic safety on winter roads, especially at night, could be enhanced.

Equipment used for ships operating in the polar regions, such as icebreakers, should consider countermeasures against freezing. This study performed a structural design that prevents freezing and tolerates thermal stress and wind pressure of the air vent louver heating blades. As boundary conditions for performing the analysis, analysis was performed when the flow rates at the inlet end were 10m/s, 20m/s 30m/s, 40m/s, and 50m/s. As a result of the analysis, if the CNT heating element can maintain the heating performance of 200°C, the blade can maintain the room temperature state except for the end of about 40mm. There are pressure drop between the front and rear of the air vent louver. It can be seen that the allowable wind speed varies depending on the design criteria. As a results, it is required to select an optimal heating temperature to prevent condensation of a blade, optimize the generation of compressive stress by thermal expansion, and trade off the wind pressure and thermal stress according to wind speed.

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.

of actual and suspicious black-ice cases that occurred during the last 10 years in the Republic of Korea. METHODS : Based on literature review, meteorological observation data associated with black-ice formation are selected: wind speed, air temperature (T), dew point temperature (Td), and relative humidity, to set minimum or maximum threshold values based on the normal distribution of each variable. In addition, weights are assigned based on the relationship among the variables to calculate the probability of occurrence. RESULTS : The threshold values are calculated using the average and standard deviation, resulting in 7.65 °C, 56.63%, 2.99 ms-1 for T-Td, relative humidity, and wind speed, respectively. Whereas the threshold value of T-Td and wind speed is set to the maximum threshold, that of the relative humidity is set to the minimum threshold value. These threshold values are applied to the diagnosis algorithm of black-ice formation, including a 1-h accumulated precipitation. CONCLUSIONS : The algorithm is expected to be utilized as a research methodology for diagnosing suspected cases of black ice.