• Geomechanics and Engineering
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• v.6 no.3
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• pp.235-248
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• 2014

Population increase and economic developments can lead to construction as well as demolition of infrastructures such as buildings, bridges, roads, etc resulting in used concrete as a primary waste product. Recycling of waste concrete to obtain the recycled concrete aggregates (RCA) for base and/or sub-base materials in road construction is a foremost application to be promoted to gain economical and sustainability benefits. As the mortar, bricks, glass and reclaimed asphalt pavement (RAP) present as constituents in RCA, it exhibits inconsistent properties and performance. In this study, six different types of RCA samples were subjected classification tests such as particle size distribution, plasticity, compaction test, unconfined compressive strength (UCS) and California bearing ratio (CBR) tests. Results were compared with those of the standard road materials used in Queensland, Australia. It was found that material type 'RM1-100/RM3-0' and 'RM1-80/RM3-20' samples are in the margin of the minimum required specifications of base materials used for high volume unbound granular roads while others are lower than that the minimum requirement.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.24-34
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• 2014

To understand urban space and make appropriate plans, the integrative analyses considering road and land use simultaneously are required. In addition, studies that involve both horizontal and vertical spaces must be taken into consideration. Therefore, the purpose of this study is to conduct a classification analysis of road network-based land use considering spatial structure. The methods of this study were as follows; first, a space syntax theory considering the structure of road network was introduced for roads. For land use, to consider both horizontal and vertical development densities of residential and commercial buildings were used. And the explanatory power of three variables-Euclidean distance, global integration and length-reflected global integration-were compared. Third, based on road as an appropriate variable, modified-IPA was conducted with land use and the results were categorized into four areas. The proposed method was applied to Gangnam-gu, a CBD area in Seoul, and results were analyzed and visualized using GIS.

• Journal of the Korean association of regional geographers
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• v.21 no.3
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• pp.553-568
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• 2015

This paper focuses on the geographical characteristics and the spatial distributions and patterns of the road names in the new address system for which all the 183 road names of Cheongju City has been used. All 183 road names in Cheongju City and their textural information are analyzed and classified into four main categories and six divisions as sub-category. Each type is mapped and its spatial patterns are discussed in order to identify the interaction between the road name and the geographical characteristics of each type. From the discussion stated in the paper, it can be inferred that the road name is not only a representative place name in an area, but also presents an important geographical feature reflecting the toponymy of the cultural and historical backgrounds of an area. Therefore, it is necessary to recognize that for road naming, various aspects such as geographical backgrounds and characteristics should be considered. These are directly related to the publicity and utilization of the road names to the public who is still unfamiliar with the new address system to be used. Finally, various geographical topics and approaches such as toponymy and spatial analysis are proposed for further geographical research, which will contribute to the extent of geographical research scopes.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• Journal of Soil and Groundwater Environment
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• v.18 no.2
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• pp.19-26
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• 2013

This study developed the classification of National Soil Quality Monitoring Network (NSQM) and its site selection criteria to meet the recently established purposes of the NSQM. The NSQM were suggested by this study to classify into the six-purposes site groups from the current classification of land uses. The six purposes site groups were 1) intensive observation sites, 2) contaminant loading sites, 3) human activity sites, 4) background sites, 5) river soil sites, and 6) sites near the groundwater quality monitoring wells. Furthermore, this study developed the site selection criteria of NSQM utilizing the accumulated NSQM data, road traffic data, chemical emission data, census, soil information, and the literature related to soil quality variation due to contaminant loads. For selecting suitable sites for NSQM, this study used road traffic, chemical emission, the distance from the contaminant sources, and population information as specific criteria. The suggested site classification and criteria were appled for the current 100 NSQM sites for evaluation. Forty sites were met to the criteria suggested by this study, but sixty sites were not met to the criteria. However, some of the sixty sites also included the obscure sites that their addresses were not apparent to find them.

• Proceedings of the Korean Society for Language and Information Conference
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• 1998.02a
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• pp.340-347
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• 1998

• 한국도로학회:학술대회논문집
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• 2010.09a
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• pp.457-460
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• 2010

• International Journal of Highway Engineering
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• v.8 no.4 s.30
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• pp.49-62
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• 2006

The republic of korea has put a great emphasis on the role of the road as widening a social infra-structural facility. Thus, vast amount of money has been invested on the road establishment. As a result, there has been fruitful outcomes in establishing the road system of the nation especially for the flat road with ease. However, in order to have more systematic and sustainable road system, we should turn our attention to more painful and high-cost regions such as mountainous districts and those are to be developed effectively. The configuration of the road is an important factor to be considered in making a decision for the road planning. Nevertheless, current road planning criterion has no such clarified and objective judging standard for figuring the configuration of the road out and, as a result, speed planning can be decided incorrectly. our research has acknowledged the necessity of estimating the configuration of the road and aimed to make it organized and sorted according to the height, slope, and the vehicle's speed. The results are as follows. First, our research made use of GIS data and classified the road into 9 different areas according to the height and the slope. Also, road classification being matched to the data of vehicle's speed, it has been shown that those characteristics of different areas have made an influence on vehicle's speed. Secondly, based on the results of the similarity between geographical classification and, vehicle's speed of sorted groups according to the height and the slope, conclusively we have classified as flat, rolling region and mountain. Since our research has made use of vehicle's speed for National Highway, it is not applicable to different functional highways. However, for the highway to be established hereafter, it can be a standard for reflection geographical characteristics.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.13-20
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• 2022

It's proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It's for the coefficient of determination (R²) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

• ETRI Journal
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• v.37 no.3
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• pp.606-616
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• 2015

In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.