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 : 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 : Previously, airport concrete pavement was designed using only aircraft gear loading without consideration of environmental loading. In this study, a multiple-regression model was developed to predict maximum tensile stress of airport concrete pavement based on finite element analysis using both environmental and B777 aircraft gear loadings. METHODS: A finite element model of airport concrete pavement and B777 aircraft main gears were fabricated to perform finite element analysis. The geometric shape of the pavement, material properties of the layers, and the loading conditions were used as input parameters for the finite element model. The sensitivity of maximum tensile stress of a concrete slab according to the variation in each input parameter was investigated by setting the ranges of the input parameters and performing finite element analysis. Based on the sensitivity analysis results, influential factors affecting the maximum tensile stress were found to be used as independent variables of the multi regression model. The maximum tensile stresses predicted by both the multiple regression model and finite element model were compared to verify the validity of the model developed in this study. RESULTS: As a result of the finite element analysis, it was determined that the maximum tensile stress developed at the bottom of the slab edge where gear loading was applied in the case that environmental loading was small. In contrast, the maximum tensile stress developed at the top of the slab center situated between the main gears in the case that the environmental loading got larger. As a result of the sensitivity analysis and multiple regression analysis, a maximum tensile stress prediction model was developed. The independent variables used included the joint spacing, slab thickness, the equivalent linear temperature difference between the top and bottom of the slab, the maximum take-off weight of a B777 aircraft, and the composite modulus of the subgrade reaction. The model was validated by comparing the predicted maximum tensile stress to the result of the finite element analysis. CONCLUSIONS : The research shown in this paper can be utilized as a precedent study for airport concrete pavement design using environmental and aircraft gear loadings simultaneously.