PURPOSES : Snow-removal performance is performed in this study to assess the feasibility of replacing calcium-chloride solution with sodium chloride solution at the minimum temperature of -5 ℃ during snowfall. METHODS : The atmospheric temperature distribution in Seoul was analyzed. The manufacturing, storage, and indoor melting performance of calcium-chloride and sodium-chloride solutions were evaluated, and on-site snow-removal performance was evaluated based on the solution type. RESULTS : According to the results of the melting performance test at -5°C, the melting capacity of the sodium chloride solution was expressed at a level exceeding 90% of that of the calcium chloride solution, indicating a similar melting performance between the two solutions. Additionally, based on the snow removal performance test using aqueous solutions, the snow removal performance of the sodium chloride solution was found to be approximately 96% compared to that of the calcium chloride solution, indicating minimal differences in snow removal performance due to changes in the type of solution. CONCLUSIONS : Similar snow-removal performance was achieved when the sodium chloride solution was used instead of calciumchloride aqueous solution at temperatures exceeding -5 ℃.

PURPOSES : The number of snowfall and the amount of snowfall are gradually increasing, and due to the characteristics of Seoul, which has a lot of traffic, it is difficult to respond quickly with a snow removal method that relies on snow removal vehicles. Therefore, it is necessary to develop an IoT based eco-friendly snow removal system that can respond to unexpected heavy snow in winter. In this study, the low temperature operation and snow removal performance of the IoT road condition snow removal detector and the snow removal system using CNT and PCM are evaluated in the climatic environment chamber. METHODS : To make artificial snow, it consists of an climatic environment chamber that can simulate a low temperature environment and equipment that can supply compressed air and cold water. Depending on the usage characteristics of the climatic environment chamber, use an air-water type snow maker. In order to make artificial snow, wet temperature, which takes into account the actual air temperature and the amount of moisture in the air, acts as the most important variable and is suitable for making snow, below –1.5 ℃. The lower the water temperature, the easier it is to freeze, so the water source was continuously supplied at 0 ℃ to 4 ℃. One of the two different pipes is connected to the water tank to supply water, and the other pipe is connected to the compressor to supply high-pressure air. Water is dispersed by compressed air in the form of many small droplets. The sprayed microscopic water particles freeze quickly in the low temperature environmental climatic chamber air and naturally fall to the floor, forming snow. Based on the KS C IEC 60068-2-1 cold resistance test standard, an integrated environmental test procedure was prepared to apply to IoT-based snow removal systems and performance evaluation was performed accordingly. The IoT based eco-friendly snow removal system is needed to in winter, so visual check and inspect the operation at the climatic chamber before setting up it to the actual site. In addition, grid type equipment was manufactured for consistent and reliable snow removal performance evaluation under controlled environmental conditions. RESULTS : The IoT-based eco-friendly snow removal system normally carried out the task of acquiring data and images without damaging the appearance or freezing in a low temperature environment. It showed clear snow removal performance in areas where PCM and CNT heating technology were applied to the concrete slab. This experiment shows that normal snow removal tasks can be carried out in low temperature environments in winter. CONCLUSIONS : The integrated environmental test procedures and grid type evaluation equipment are applied to low temperature operation and snow removal performance evaluation of snow removal systems. In the climatic environment chamber, where low temperature environments can be simulated, artificial snow is created regardless of the season to derive quantitative experimental results on snow removal performance. PCM and CNT heating technology showed high snow removal performance. The system is expected to be applied to road site situations to preemptively respond to unexpected heavy snow in winter.

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.

PURPOSES : In this study, a method for evaluating concrete bridge deck deterioration using three-dimensional (3D) ground penetrating radar (GPR) survey data and its in situ application are discussed. METHODS : Field surveys are conducted on two bridges in Yongsan-gu (Bridge A) and Seodaemun-gu (Bridge B) in Seoul using 3D GPR. The obtained survey data are used to calculate the dielectric constant map of each bridge using the extended common midpoint method. In addition, random points on both bridges are selected for the chloride content test in accordance with the KS F 2713 standard. The results from the dielectric constant map and chloride content test are compared. RESULTS : For Bridge A, it is discovered that the percentage of sections with a dielectric constant of 5.0 or less is 1.57%, whereas that above 5.0 is 98.43%; this indicates that the percentage of deteriorated sections for Bridge A is low. Meanwhile, for Bridge B, the dielectric constants calculated for the entire bridge exceed 5.0, which suggests no deterioration for Bridge B. Moreover, all the points selected for the chloride content test have less than 0.15% chloride content and have dielectric constants ranging from 5.0 to 7.0, which are favorable condition for the bridge deck. CONCLUSIONS : The analysis results of the dielectric constants of the concrete bridge deck obtained from the 3D GPR system are consistent with the actual chloride content results. Furthermore, additional verification of this method through field surveys on bridge sections with severe deterioration is highly recommended for future improvements.

PURPOSES: This study was conducted to investigate the causes of damage and to suggest proper repair methods for the sections in which a number of transverse cracks and faulting occurred in JCP (Jointed Concrete Pavement) slabs during the early-age performance period. METHODS: Field crack survey, FWD (Falling Weight Deflectometer) investigation, dowel bar installation condition survey, longitudinal profile survey, and in-situ core specimen inspection were conducted. . RESULTS : As a result of the analysis, it was found that there was no decline in the composite modulus of the subgrade reaction (k-value). The dowel bars were properly installed, but the LTE (Load Transfer Efficiency) of the joint and transverse cracks were analyzed to be very low. In addition, it was found that there are voids in the bottom of the slab at the joints and corners. Due to the excessive built-in curling in the early age of concrete pavement construction, upward curling displacement occurred at the joints and corners, resulting in voids at the bottom of the slab. As a result, it was found that transverse cracks occurred due to the defective joint LTE. CONCLUSIONS: Excessive built-in curling can cause early age cracks in the JCP slabs. In order to minimize the occurrence of reflection cracks after the application of the asphalt overlay in the future, concepts of alternative repair methods were proposed.